R2: An Interactive Online Portal for Tumor Subgroup Gene Expression and Survival Analyses, Intended for Biomedical Researchers Genomic data from The Cancer Genome Atlas (TCGA) project has enabled comprehensive molecular profiling of diverse cancer types. The extensive sample size within TCGA provides an invaluable resource for investigating tumor heterogeneity. Effective exploration of this dataset by researchers and clinicians is essential for discovering novel therapeutic and diagnostic biomarkers. While numerous computational tools have been developed to analyze specific aspects of TCGA data, there remains a need for platforms that facilitate the study of gene expression variability and its association with clinical outcomes across tumors. Here, we introduce the R2 Platform , an intuitive and interactive web portal designed for in-depth analysis of TCGA gene expression data. The portal leverages TCGA Level 3 RNA-seq and clinical data from 31 cancer types. With its user-f...
The Cancer Genome Atlas Program (TCGA) is one of the cornerstones of cancer research. Every sample has a unique identifier, also known as barcode. A basic understanding of the naming convention is very helpful to quickly assess the 'type of sample' that you are working with. The sample type is contained in the numerical part of the 4th element of a barcode, that can be up to 7 parts. Barcodes most commonly are composed of 4 parts, which is informative for most use cases. Code Definition 1 Primary Solid Tumor 2 Recurrent Solid Tumor 3 Primary Blood Derived Cancer - Peripheral Blood 4 Recurrent Blood Derived Cancer - Bone Marrow 5 Additional - New Primary 6 Metastatic 7 Additional Metastatic 8 Human Tumor Original Cells 9 Primary Blood Derived Cancer - Bone Marrow 10 Blood Derived Normal 11 Solid Tissue Normal 12 Buccal Cell Normal 13 EBV Immortalized Normal 14 Bone Marrow Normal 15 sample type 15 16 sample type 16 20 Control Analyte 40 Recur...
Quite frequently, you may want to provide a quick and easy to grasp plot that shows the expression of a couple of genes, segregated over a few groups. With single cell mRNA data, this also needs to take into account the fact that a gene if frequently not detected in all of the cells. Enter the bubble plot. This visualization will show the data much like a heatmap, where every sub-group is represented by a proportional circle. The dcolor here then reflects the average intensity value, while the circle size denotes a percentage of the cells that show expression. Within R2, we have also got this type of viaualization implemented, making it easy to use such insightful graphs with ease. How the circles are scaled can also be defined in different ways. You can look at this from the perspective of the total data set size, and represent the data as a percentage of all the cells. Alternatively, you can also represent the data as a percentage of the cells represented within a group. This l...
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